Recognition of common areas in a Web page using visual information: a possible application in a page classification

A new, hierarchical representation that includes browser screen coordinates for every HTML object in a page is proposed that shows that a Naive Bayes classifier clearly outperforms the same classifier using only information about the content of documents.

Abstract

Extracting and processing information from Web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the Web. Common approach in the extraction process is to represent a page as a "bag of words" and then to perform additional processing on such a flat representation. In this paper we propose, a new, hierarchical representation that includes browser screen coordinates for every HTML object in a page. Using visual information one is able to define heuristics for the recognition of common page areas such as header, left and right menu, footer and center of a page. We show in initial experiments that using our heuristics defined objects are recognized properly in 73% of cases. Finally, we show that a Naive Bayes classifier, taking into account the proposed representation, clearly outperforms the same classifier using only information about the content of documents.